Chi-Square and Correlation- TOPIC 4 DQ 1

Chi-Square and Correlation- TOPIC 4 DQ 1



Correlation is a common statistic to measure a general linear relationship between two variables. Explain why correlation does not equal causation.







Explain Why Correlation Does Not Equal Causation

Causation and correlation are some of the most misunderstood and misused terms. In some instances, correlation and causation are used interchangeably. Understanding both terms is therefore important not only in making conclusions but also in making the correct conclusions. Correlation will therefore not equal causation. Correlation can therefore be defined as a statistical technique that indicates the linear relationship between a pair of variables and how a change in one will cause a change in the other (Rohrer, 2018). Correlation, therefore, does not indicate the reasons between relationships between a pair of variables but only indicates that the relationship exists (Joly, 2017). An example of correlation that is observed during summer is the sale of sunglasses being correlated with the sale of ice creams.

On the other hand, causation explains the relationship of variables. In causation, a change in one variable will therefore cause a change in another variable. In causation, a change in one variable is, therefore, the cause of the change in the associated variable (Rohrer, 2018). Causation is therefore referred to as cause and effect (Joly, 2017). An example of a causal relationship is when a person exercises to burn calories. The number of calories burnt increases every time a person exercises. Exercise is therefore causing the burning of calories.

The correlation of two variables will not mean that one causes the other. In this regard, correlation will not mean causality. In most instances, correlation will result from coincidences. When making observations, individuals are therefore more likely to observe correlations as they do not have detailed information. On the other hand, when detailed information is available, transparency will arise, enabling one to see actual causal relationships (Rohrer, 2018).




Joly, E. (2017). Baseball and jet lag: Correlation does not imply causation. Proceedings of the National Academy of Sciences, 114(16), E3168.

Rohrer, J. M. (2018). Thinking Clearly About Correlations and Causation: Graphical Causal Models for Observational Data. Advances in Methods and Practices in Psychological Science, 1(1), 27–42.


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